
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCAmelCase       =					TypeVar('''T''')
UpperCAmelCase       =					TypeVar('''U''')







class 			A_  (     Generic[T, U]  ):




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case			):
		lowercase	    =       key
		lowercase	    =       val
		lowercase	    =       None
		lowercase	    =       None





	def __repr__(			self			):
		return (
		    F'''Node: key: {self.key}, val: {self.val}, '''
		    F'''has next: {bool(self.next			)}, has prev: {bool(self.prev			)}'''
		)







class 			A_  (     Generic[T, U]  ):




	'''simple docstring'''


	def __init__(			self			):
		lowercase	    =       DoubleLinkedListNode(snake_case						,       snake_case			)
		lowercase	    =       DoubleLinkedListNode(snake_case						,       snake_case			)
		lowercase    ,   lowercase	    =       self.rear, self.head





	def __repr__(			self			):
		lowercase	    =       ['DoubleLinkedList']
		lowercase	    =       self.head
		while node.next is not None:
			rep.append(str(snake_case			)			)
			lowercase	    =       node.next
		rep.append(str(self.rear			)			)
		return ",\n    ".join(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       self.rear.prev

		# All nodes other than self.head are guaranteed to have non-None previous
		assert previous is not None

		lowercase	    =       node
		lowercase	    =       previous
		lowercase	    =       node
		lowercase	    =       self.rear





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):

		if node.prev is None or node.next is None:
			return None

		lowercase	    =       node.next
		lowercase	    =       node.prev
		lowercase	    =       None
		lowercase	    =       None
		return node







class 			A_  (     Generic[T, U]  ):




	'''simple docstring'''


	_UpperCamelCase	:	dict[Callable[[T], U], LRUCache[T, U]]									=			{}





	def __init__(			self						,       snake_case			):
		lowercase	    =       DoubleLinkedList()
		lowercase	    =       capacity
		lowercase	    =       0
		lowercase	    =       0
		lowercase	    =       0
		lowercase	    =       {}





	def __repr__(			self			):
		return (
		    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
		    F'''capacity={self.capacity}, current size={self.num_keys})'''
		)





	def __contains__(			self						,       snake_case			):
		return key in self.cache





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		# Note: pythonic interface would throw KeyError rather than return None

		if key in self.cache:
			self.hits += 1
			lowercase	    =       self.cache[key]
			lowercase	    =       self.list.remove(self.cache[key]			)
			assert node == value_node

			# node is guaranteed not None because it is in self.cache
			assert node is not None
			self.list.add(snake_case			)
			return node.val
		self.miss += 1
		return None





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):

		if key not in self.cache:
			if self.num_keys >= self.capacity:
				# delete first node (oldest) when over capacity
				lowercase	    =       self.list.head.next

				# guaranteed to have a non-None first node when num_keys > 0
				# explain to type checker via assertions
				assert first_node is not None
				assert first_node.key is not None
				assert (
				    self.list.remove(snake_case			) is not None
				)  # node guaranteed to be in list assert node.key is not None

				del self.cache[first_node.key]
				self.num_keys -= 1
			lowercase	    =       DoubleLinkedListNode(snake_case						,       snake_case			)
			self.list.add(self.cache[key]			)
			self.num_keys += 1

		else:
			# bump node to the end of the list, update value
			lowercase	    =       self.list.remove(self.cache[key]			)
			assert node is not None  # node guaranteed to be in list
			lowercase	    =       value
			self.list.add(snake_case			)





	@classmethod
	def       SCREAMING_SNAKE_CASE__		(			cls						,       snake_case = 128			):

		def cache_decorator_inner(snake_case			) -> Callable[..., U]:
			def cache_decorator_wrapper(*snake_case			) -> U:
				if func not in cls.decorator_function_to_instance_map:
					lowercase	    =       LRUCache(snake_case			)

				lowercase	    =       cls.decorator_function_to_instance_map[func].get(args[0]			)
				if result is None:
					lowercase	    =       func(*snake_case			)
					cls.decorator_function_to_instance_map[func].put(args[0]						,       snake_case			)
				return result

			def cache_info() -> LRUCache[T, U]:
				return cls.decorator_function_to_instance_map[func]

			setattr(snake_case						,       'cache_info'						,       snake_case			)  # noqa: B010

			return cache_decorator_wrapper

		return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()









"""simple docstring"""





import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow






@is_pipeline_test
@require_torch
class 	a		(   unittest.TestCase						):






    @require_torch
    def   UpperCamelCase			(			self	:				str   )	->						str:
      lowerCamelCase_       	=		pipeline(
          task='zero-shot-audio-classification'			,		model='hf-internal-testing/tiny-clap-htsat-unfused'   )
      lowerCamelCase_       	=		load_dataset('ashraq/esc50'   )
      lowerCamelCase_       	=		dataset['train']['audio'][-1]['array']
      lowerCamelCase_       	=		audio_classifier(__SCREAMING_SNAKE_CASE			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[{'score': 0.501, 'label': 'Sound of a dog'}, {'score': 0.499, 'label': 'Sound of vaccum cleaner'}]			,		)






    @unittest.skip('No models are available in TF'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      pass






    @slow
    @require_torch
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		pipeline(
          task='zero-shot-audio-classification'			,		model='laion/clap-htsat-unfused'			,		)
      # This is an audio of a dog
      lowerCamelCase_       	=		load_dataset('ashraq/esc50'   )
      lowerCamelCase_       	=		dataset['train']['audio'][-1]['array']
      lowerCamelCase_       	=		audio_classifier(__SCREAMING_SNAKE_CASE			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              {'score': 0.999, 'label': 'Sound of a dog'},
              {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
          ]			,		)

      lowerCamelCase_       	=		audio_classifier([audio] * 5			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              [
                  {'score': 0.999, 'label': 'Sound of a dog'},
                  {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
              ],
          ]
          * 5			,		)
      lowerCamelCase_       	=		audio_classifier(
          [audio] * 5			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']			,		batch_size=5   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              [
                  {'score': 0.999, 'label': 'Sound of a dog'},
                  {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
              ],
          ]
          * 5			,		)






    @unittest.skip('No models are available in TF'   )
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      pass




